code: def test_seed_implies_deterministic_results(self):
      dim = 20
      num_results = 100
      seed = test_util.test_seed()
      sample1 = sample_halton_sequence_lib.sample_halton_sequence(
          dim, num_results=num_results, seed=seed
      )
      if tf.executing_eagerly() and not JAX_MODE:
          tf.random.set_seed(seed)
      sample2 = sample_halton_sequence_lib.sample_halton_sequence(
          dim, num_results=num_results, seed=seed
      )
      [sample1_, sample2_] = self.evaluate([sample1, sample2])
      self.assertAllClose(sample1_, sample2_, atol=0.0, rtol=1e-6)
  
file_path: probability/tensorflow_probability/python/mcmc/sample_halton_sequence_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import monte_carlo
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import sample_halton_sequence_lib
label_desc: Use image node_id 10 for calling the HaltonSequenceTest obj's underlying member method code with example usage: obj.test_seed_implies_deterministic_results() without return types